Overview

Dataset statistics

Number of variables18
Number of observations4250
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory597.8 KiB
Average record size in memory144.0 B

Variable types

Numeric15
Categorical3

Alerts

number_vmail_messages is highly overall correlated with voice_mail_planHigh correlation
total_day_charge is highly overall correlated with total_day_minutesHigh correlation
total_day_minutes is highly overall correlated with total_day_chargeHigh correlation
total_eve_charge is highly overall correlated with total_eve_minutesHigh correlation
total_eve_minutes is highly overall correlated with total_eve_chargeHigh correlation
total_intl_charge is highly overall correlated with total_intl_minutesHigh correlation
total_intl_minutes is highly overall correlated with total_intl_chargeHigh correlation
total_night_charge is highly overall correlated with total_night_minutesHigh correlation
total_night_minutes is highly overall correlated with total_night_chargeHigh correlation
voice_mail_plan is highly overall correlated with number_vmail_messagesHigh correlation
international_plan is highly imbalanced (55.3%)Imbalance
number_vmail_messages has 3139 (73.9%) zerosZeros
number_customer_service_calls has 886 (20.8%) zerosZeros

Reproduction

Analysis started2024-04-30 07:19:00.407744
Analysis finished2024-04-30 07:19:55.202016
Duration54.79 seconds
Software versionydata-profiling vv4.7.0
Download configurationconfig.json

Variables

account_length
Real number (ℝ)

Distinct215
Distinct (%)5.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean100.23624
Minimum1
Maximum243
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size33.3 KiB
2024-04-30T07:19:55.332442image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile35.45
Q173
median100
Q3127
95-th percentile167
Maximum243
Range242
Interquartile range (IQR)54

Descriptive statistics

Standard deviation39.698401
Coefficient of variation (CV)0.3960484
Kurtosis-0.13217477
Mean100.23624
Median Absolute Deviation (MAD)27
Skewness0.12232732
Sum426004
Variance1575.963
MonotonicityNot monotonic
2024-04-30T07:19:55.602652image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
90 53
 
1.2%
87 51
 
1.2%
93 50
 
1.2%
105 48
 
1.1%
100 48
 
1.1%
120 48
 
1.1%
116 47
 
1.1%
98 47
 
1.1%
127 47
 
1.1%
112 46
 
1.1%
Other values (205) 3765
88.6%
ValueCountFrequency (%)
1 7
0.2%
2 2
 
< 0.1%
3 7
0.2%
4 2
 
< 0.1%
5 2
 
< 0.1%
6 2
 
< 0.1%
7 5
0.1%
8 1
 
< 0.1%
9 2
 
< 0.1%
10 3
0.1%
ValueCountFrequency (%)
243 1
 
< 0.1%
232 2
< 0.1%
225 2
< 0.1%
224 2
< 0.1%
222 2
< 0.1%
221 1
 
< 0.1%
217 3
0.1%
216 1
 
< 0.1%
215 1
 
< 0.1%
212 1
 
< 0.1%

international_plan
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size33.3 KiB
0
3854 
1
396 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4250
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 3854
90.7%
1 396
 
9.3%

Length

2024-04-30T07:19:55.805432image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-30T07:19:55.998382image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 3854
90.7%
1 396
 
9.3%

Most occurring characters

ValueCountFrequency (%)
0 3854
90.7%
1 396
 
9.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4250
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 3854
90.7%
1 396
 
9.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4250
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 3854
90.7%
1 396
 
9.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4250
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 3854
90.7%
1 396
 
9.3%

voice_mail_plan
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size33.3 KiB
0
3138 
1
1112 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4250
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 3138
73.8%
1 1112
 
26.2%

Length

2024-04-30T07:19:56.182709image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-30T07:19:56.380711image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 3138
73.8%
1 1112
 
26.2%

Most occurring characters

ValueCountFrequency (%)
0 3138
73.8%
1 1112
 
26.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4250
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 3138
73.8%
1 1112
 
26.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4250
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 3138
73.8%
1 1112
 
26.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4250
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 3138
73.8%
1 1112
 
26.2%

number_vmail_messages
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct46
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.6317647
Minimum0
Maximum52
Zeros3139
Zeros (%)73.9%
Negative0
Negative (%)0.0%
Memory size33.3 KiB
2024-04-30T07:19:56.589575image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q316
95-th percentile36
Maximum52
Range52
Interquartile range (IQR)16

Descriptive statistics

Standard deviation13.439882
Coefficient of variation (CV)1.7610451
Kurtosis0.27303834
Mean7.6317647
Median Absolute Deviation (MAD)0
Skewness1.373091
Sum32435
Variance180.63043
MonotonicityNot monotonic
2024-04-30T07:19:56.828251image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
0 3139
73.9%
31 69
 
1.6%
28 58
 
1.4%
24 57
 
1.3%
29 57
 
1.3%
33 55
 
1.3%
27 54
 
1.3%
26 53
 
1.2%
30 47
 
1.1%
32 47
 
1.1%
Other values (36) 614
 
14.4%
ValueCountFrequency (%)
0 3139
73.9%
4 1
 
< 0.1%
6 2
 
< 0.1%
8 2
 
< 0.1%
10 4
 
0.1%
11 2
 
< 0.1%
12 10
 
0.2%
13 3
 
0.1%
14 7
 
0.2%
15 12
 
0.3%
ValueCountFrequency (%)
52 1
 
< 0.1%
50 2
 
< 0.1%
49 3
 
0.1%
48 4
 
0.1%
47 4
 
0.1%
46 7
0.2%
45 10
0.2%
44 7
0.2%
43 13
0.3%
42 17
0.4%

total_day_minutes
Real number (ℝ)

HIGH CORRELATION 

Distinct1843
Distinct (%)43.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean180.2596
Minimum0
Maximum351.5
Zeros2
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size33.3 KiB
2024-04-30T07:19:57.075439image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile91.59
Q1143.325
median180.45
Q3216.2
95-th percentile271.055
Maximum351.5
Range351.5
Interquartile range (IQR)72.875

Descriptive statistics

Standard deviation54.012373
Coefficient of variation (CV)0.2996366
Kurtosis-0.056709716
Mean180.2596
Median Absolute Deviation (MAD)36.6
Skewness-0.0069102298
Sum766103.3
Variance2917.3365
MonotonicityNot monotonic
2024-04-30T07:19:57.328911image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
189.3 10
 
0.2%
180 9
 
0.2%
154 8
 
0.2%
177.1 8
 
0.2%
184.5 8
 
0.2%
209.9 7
 
0.2%
189.8 7
 
0.2%
138.7 7
 
0.2%
165.4 7
 
0.2%
174 7
 
0.2%
Other values (1833) 4172
98.2%
ValueCountFrequency (%)
0 2
< 0.1%
2.6 1
< 0.1%
6.6 1
< 0.1%
7.2 1
< 0.1%
7.8 1
< 0.1%
7.9 1
< 0.1%
25.9 1
< 0.1%
27 1
< 0.1%
29.9 1
< 0.1%
30.9 1
< 0.1%
ValueCountFrequency (%)
351.5 1
< 0.1%
346.8 1
< 0.1%
345.3 1
< 0.1%
338.4 1
< 0.1%
337.4 1
< 0.1%
335.5 1
< 0.1%
334.3 1
< 0.1%
332.9 1
< 0.1%
332.1 1
< 0.1%
329.8 1
< 0.1%

total_day_calls
Real number (ℝ)

Distinct120
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean99.907294
Minimum0
Maximum165
Zeros2
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size33.3 KiB
2024-04-30T07:19:57.590711image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile67
Q187
median100
Q3113
95-th percentile133
Maximum165
Range165
Interquartile range (IQR)26

Descriptive statistics

Standard deviation19.850817
Coefficient of variation (CV)0.19869237
Kurtosis0.19359365
Mean99.907294
Median Absolute Deviation (MAD)13
Skewness-0.085812463
Sum424606
Variance394.05495
MonotonicityNot monotonic
2024-04-30T07:19:57.838648image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
105 101
 
2.4%
95 97
 
2.3%
110 92
 
2.2%
94 92
 
2.2%
112 90
 
2.1%
102 89
 
2.1%
97 88
 
2.1%
107 87
 
2.0%
100 85
 
2.0%
101 84
 
2.0%
Other values (110) 3345
78.7%
ValueCountFrequency (%)
0 2
< 0.1%
30 1
 
< 0.1%
34 1
 
< 0.1%
35 1
 
< 0.1%
36 1
 
< 0.1%
40 2
< 0.1%
42 1
 
< 0.1%
44 4
0.1%
45 3
0.1%
46 1
 
< 0.1%
ValueCountFrequency (%)
165 1
 
< 0.1%
160 2
 
< 0.1%
158 2
 
< 0.1%
157 2
 
< 0.1%
156 3
 
0.1%
152 2
 
< 0.1%
151 6
0.1%
150 3
 
0.1%
148 6
0.1%
147 8
0.2%

total_day_charge
Real number (ℝ)

HIGH CORRELATION 

Distinct1843
Distinct (%)43.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30.644682
Minimum0
Maximum59.76
Zeros2
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size33.3 KiB
2024-04-30T07:19:58.088025image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile15.5735
Q124.365
median30.68
Q336.75
95-th percentile46.081
Maximum59.76
Range59.76
Interquartile range (IQR)12.385

Descriptive statistics

Standard deviation9.182096
Coefficient of variation (CV)0.29963097
Kurtosis-0.056584435
Mean30.644682
Median Absolute Deviation (MAD)6.225
Skewness-0.0069125262
Sum130239.9
Variance84.310888
MonotonicityNot monotonic
2024-04-30T07:19:58.339194image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
32.18 10
 
0.2%
30.6 9
 
0.2%
26.18 8
 
0.2%
30.11 8
 
0.2%
31.37 8
 
0.2%
35.68 7
 
0.2%
32.27 7
 
0.2%
23.58 7
 
0.2%
28.12 7
 
0.2%
29.58 7
 
0.2%
Other values (1833) 4172
98.2%
ValueCountFrequency (%)
0 2
< 0.1%
0.44 1
< 0.1%
1.12 1
< 0.1%
1.22 1
< 0.1%
1.33 1
< 0.1%
1.34 1
< 0.1%
4.4 1
< 0.1%
4.59 1
< 0.1%
5.08 1
< 0.1%
5.25 1
< 0.1%
ValueCountFrequency (%)
59.76 1
< 0.1%
58.96 1
< 0.1%
58.7 1
< 0.1%
57.53 1
< 0.1%
57.36 1
< 0.1%
57.04 1
< 0.1%
56.83 1
< 0.1%
56.59 1
< 0.1%
56.46 1
< 0.1%
56.07 1
< 0.1%

total_eve_minutes
Real number (ℝ)

HIGH CORRELATION 

Distinct1773
Distinct (%)41.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean200.17391
Minimum0
Maximum359.3
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size33.3 KiB
2024-04-30T07:19:58.582344image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile118.2
Q1165.925
median200.7
Q3233.775
95-th percentile282.71
Maximum359.3
Range359.3
Interquartile range (IQR)67.85

Descriptive statistics

Standard deviation50.249518
Coefficient of variation (CV)0.25102931
Kurtosis0.043453202
Mean200.17391
Median Absolute Deviation (MAD)33.7
Skewness-0.030414586
Sum850739.1
Variance2525.0141
MonotonicityNot monotonic
2024-04-30T07:19:58.819012image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
230.9 10
 
0.2%
199.7 9
 
0.2%
194 9
 
0.2%
187.5 9
 
0.2%
169.9 9
 
0.2%
195.5 8
 
0.2%
221.1 8
 
0.2%
223.5 8
 
0.2%
211.5 8
 
0.2%
230 8
 
0.2%
Other values (1763) 4164
98.0%
ValueCountFrequency (%)
0 1
< 0.1%
22.3 1
< 0.1%
37.8 1
< 0.1%
41.7 1
< 0.1%
42.2 1
< 0.1%
42.5 1
< 0.1%
43.9 1
< 0.1%
47.3 1
< 0.1%
48.1 1
< 0.1%
49.2 1
< 0.1%
ValueCountFrequency (%)
359.3 1
< 0.1%
352.1 1
< 0.1%
351.6 1
< 0.1%
349.4 1
< 0.1%
348.5 1
< 0.1%
347.3 1
< 0.1%
345.1 1
< 0.1%
344.9 1
< 0.1%
344 1
< 0.1%
341.3 1
< 0.1%

total_eve_calls
Real number (ℝ)

Distinct123
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean100.17647
Minimum0
Maximum170
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size33.3 KiB
2024-04-30T07:20:00.184249image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile67
Q187
median100
Q3114
95-th percentile133
Maximum170
Range170
Interquartile range (IQR)27

Descriptive statistics

Standard deviation19.908591
Coefficient of variation (CV)0.1987352
Kurtosis0.11459972
Mean100.17647
Median Absolute Deviation (MAD)13
Skewness-0.020811824
Sum425750
Variance396.352
MonotonicityNot monotonic
2024-04-30T07:20:00.565984image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
105 98
 
2.3%
103 96
 
2.3%
91 95
 
2.2%
97 91
 
2.1%
108 88
 
2.1%
94 88
 
2.1%
96 88
 
2.1%
88 87
 
2.0%
101 86
 
2.0%
104 85
 
2.0%
Other values (113) 3348
78.8%
ValueCountFrequency (%)
0 1
 
< 0.1%
12 1
 
< 0.1%
36 1
 
< 0.1%
38 1
 
< 0.1%
43 1
 
< 0.1%
44 2
 
< 0.1%
45 1
 
< 0.1%
46 5
0.1%
47 1
 
< 0.1%
48 6
0.1%
ValueCountFrequency (%)
170 1
 
< 0.1%
169 1
 
< 0.1%
168 1
 
< 0.1%
159 1
 
< 0.1%
157 1
 
< 0.1%
156 1
 
< 0.1%
155 5
0.1%
154 3
0.1%
153 1
 
< 0.1%
152 6
0.1%

total_eve_charge
Real number (ℝ)

HIGH CORRELATION 

Distinct1572
Distinct (%)37.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.015012
Minimum0
Maximum30.54
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size33.3 KiB
2024-04-30T07:20:00.947073image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile10.05
Q114.1025
median17.06
Q319.8675
95-th percentile24.031
Maximum30.54
Range30.54
Interquartile range (IQR)5.765

Descriptive statistics

Standard deviation4.271212
Coefficient of variation (CV)0.2510261
Kurtosis0.043329494
Mean17.015012
Median Absolute Deviation (MAD)2.86
Skewness-0.030387891
Sum72313.8
Variance18.243252
MonotonicityNot monotonic
2024-04-30T07:20:01.345187image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14.25 13
 
0.3%
18.79 13
 
0.3%
16.12 13
 
0.3%
15.9 12
 
0.3%
16.97 12
 
0.3%
18.96 11
 
0.3%
17.09 10
 
0.2%
16.8 10
 
0.2%
19.63 10
 
0.2%
17.54 9
 
0.2%
Other values (1562) 4137
97.3%
ValueCountFrequency (%)
0 1
< 0.1%
1.9 1
< 0.1%
3.21 1
< 0.1%
3.54 1
< 0.1%
3.59 1
< 0.1%
3.61 1
< 0.1%
3.73 1
< 0.1%
4.02 1
< 0.1%
4.09 1
< 0.1%
4.18 1
< 0.1%
ValueCountFrequency (%)
30.54 1
< 0.1%
29.93 1
< 0.1%
29.89 1
< 0.1%
29.7 1
< 0.1%
29.62 1
< 0.1%
29.52 1
< 0.1%
29.33 1
< 0.1%
29.32 1
< 0.1%
29.24 1
< 0.1%
29.01 1
< 0.1%

total_night_minutes
Real number (ℝ)

HIGH CORRELATION 

Distinct1757
Distinct (%)41.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean200.52788
Minimum0
Maximum395
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size33.3 KiB
2024-04-30T07:20:01.609931image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile118.09
Q1167.225
median200.45
Q3234.7
95-th percentile282.71
Maximum395
Range395
Interquartile range (IQR)67.475

Descriptive statistics

Standard deviation50.353548
Coefficient of variation (CV)0.25110497
Kurtosis0.11485358
Mean200.52788
Median Absolute Deviation (MAD)33.55
Skewness0.0084908193
Sum852243.5
Variance2535.4798
MonotonicityNot monotonic
2024-04-30T07:20:01.858643image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
186.2 11
 
0.3%
208.9 10
 
0.2%
240 8
 
0.2%
224.7 8
 
0.2%
190.5 8
 
0.2%
228.1 8
 
0.2%
194.3 8
 
0.2%
193.6 8
 
0.2%
221.7 8
 
0.2%
169.4 8
 
0.2%
Other values (1747) 4165
98.0%
ValueCountFrequency (%)
0 1
< 0.1%
23.2 1
< 0.1%
43.7 1
< 0.1%
45 1
< 0.1%
46.7 1
< 0.1%
47.4 1
< 0.1%
50.1 2
< 0.1%
53.3 1
< 0.1%
54 1
< 0.1%
54.5 1
< 0.1%
ValueCountFrequency (%)
395 1
< 0.1%
381.9 1
< 0.1%
381.6 1
< 0.1%
377.5 1
< 0.1%
367.7 1
< 0.1%
364.9 1
< 0.1%
359.9 1
< 0.1%
355.1 1
< 0.1%
352.5 1
< 0.1%
352.2 1
< 0.1%

total_night_calls
Real number (ℝ)

Distinct128
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean99.839529
Minimum0
Maximum175
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size33.3 KiB
2024-04-30T07:20:02.112963image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile67
Q186
median100
Q3113
95-th percentile132
Maximum175
Range175
Interquartile range (IQR)27

Descriptive statistics

Standard deviation20.09322
Coefficient of variation (CV)0.20125515
Kurtosis0.077218359
Mean99.839529
Median Absolute Deviation (MAD)14
Skewness0.0052731102
Sum424318
Variance403.73748
MonotonicityNot monotonic
2024-04-30T07:20:02.348984image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
105 100
 
2.4%
99 92
 
2.2%
95 91
 
2.1%
102 90
 
2.1%
94 88
 
2.1%
91 88
 
2.1%
98 87
 
2.0%
104 87
 
2.0%
100 86
 
2.0%
109 85
 
2.0%
Other values (118) 3356
79.0%
ValueCountFrequency (%)
0 1
 
< 0.1%
33 1
 
< 0.1%
36 1
 
< 0.1%
38 2
< 0.1%
40 1
 
< 0.1%
41 1
 
< 0.1%
42 4
0.1%
43 1
 
< 0.1%
44 1
 
< 0.1%
46 3
0.1%
ValueCountFrequency (%)
175 1
< 0.1%
170 1
< 0.1%
165 1
< 0.1%
164 1
< 0.1%
161 1
< 0.1%
160 1
< 0.1%
159 2
< 0.1%
158 2
< 0.1%
157 2
< 0.1%
156 2
< 0.1%

total_night_charge
Real number (ℝ)

HIGH CORRELATION 

Distinct992
Distinct (%)23.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.0238918
Minimum0
Maximum17.77
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size33.3 KiB
2024-04-30T07:20:02.608134image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5.3145
Q17.5225
median9.02
Q310.56
95-th percentile12.7255
Maximum17.77
Range17.77
Interquartile range (IQR)3.0375

Descriptive statistics

Standard deviation2.2659218
Coefficient of variation (CV)0.2511025
Kurtosis0.11486517
Mean9.0238918
Median Absolute Deviation (MAD)1.51
Skewness0.008444754
Sum38351.54
Variance5.1344017
MonotonicityNot monotonic
2024-04-30T07:20:02.848657image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9.4 18
 
0.4%
10.8 17
 
0.4%
9.63 17
 
0.4%
8.15 17
 
0.4%
9.66 16
 
0.4%
9.76 15
 
0.4%
8.82 15
 
0.4%
10.49 15
 
0.4%
7.69 14
 
0.3%
8.57 14
 
0.3%
Other values (982) 4092
96.3%
ValueCountFrequency (%)
0 1
< 0.1%
1.04 1
< 0.1%
1.97 1
< 0.1%
2.03 1
< 0.1%
2.1 1
< 0.1%
2.13 1
< 0.1%
2.25 2
< 0.1%
2.4 1
< 0.1%
2.43 1
< 0.1%
2.45 1
< 0.1%
ValueCountFrequency (%)
17.77 1
< 0.1%
17.19 1
< 0.1%
17.17 1
< 0.1%
16.99 1
< 0.1%
16.55 1
< 0.1%
16.42 1
< 0.1%
16.2 1
< 0.1%
15.98 1
< 0.1%
15.86 1
< 0.1%
15.85 1
< 0.1%

total_intl_minutes
Real number (ℝ)

HIGH CORRELATION 

Distinct168
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.256071
Minimum0
Maximum20
Zeros22
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size33.3 KiB
2024-04-30T07:20:03.098593image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5.7
Q18.5
median10.3
Q312
95-th percentile14.6
Maximum20
Range20
Interquartile range (IQR)3.5

Descriptive statistics

Standard deviation2.7601017
Coefficient of variation (CV)0.26911883
Kurtosis0.70295119
Mean10.256071
Median Absolute Deviation (MAD)1.8
Skewness-0.24135954
Sum43588.3
Variance7.6181615
MonotonicityNot monotonic
2024-04-30T07:20:03.341935image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11.1 75
 
1.8%
9.8 73
 
1.7%
11.4 73
 
1.7%
10.2 72
 
1.7%
10.9 71
 
1.7%
11.3 70
 
1.6%
10.1 69
 
1.6%
9.7 68
 
1.6%
10.5 66
 
1.6%
9.5 66
 
1.6%
Other values (158) 3547
83.5%
ValueCountFrequency (%)
0 22
0.5%
0.4 1
 
< 0.1%
1.1 2
 
< 0.1%
1.3 1
 
< 0.1%
2 2
 
< 0.1%
2.1 2
 
< 0.1%
2.2 2
 
< 0.1%
2.4 1
 
< 0.1%
2.5 1
 
< 0.1%
2.6 1
 
< 0.1%
ValueCountFrequency (%)
20 1
 
< 0.1%
19.7 2
< 0.1%
19.3 1
 
< 0.1%
19.2 1
 
< 0.1%
18.9 1
 
< 0.1%
18.5 1
 
< 0.1%
18.4 1
 
< 0.1%
18.3 1
 
< 0.1%
18.2 2
< 0.1%
18 3
0.1%

total_intl_calls
Real number (ℝ)

Distinct21
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.4263529
Minimum0
Maximum20
Zeros22
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size33.3 KiB
2024-04-30T07:20:03.572653image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q13
median4
Q36
95-th percentile9
Maximum20
Range20
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.4630691
Coefficient of variation (CV)0.55645565
Kurtosis3.2632275
Mean4.4263529
Median Absolute Deviation (MAD)1
Skewness1.3601222
Sum18812
Variance6.0667095
MonotonicityNot monotonic
2024-04-30T07:20:03.785477image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
3 847
19.9%
4 795
18.7%
2 644
15.2%
5 598
14.1%
6 408
9.6%
7 272
 
6.4%
1 226
 
5.3%
8 153
 
3.6%
9 126
 
3.0%
10 59
 
1.4%
Other values (11) 122
 
2.9%
ValueCountFrequency (%)
0 22
 
0.5%
1 226
 
5.3%
2 644
15.2%
3 847
19.9%
4 795
18.7%
5 598
14.1%
6 408
9.6%
7 272
 
6.4%
8 153
 
3.6%
9 126
 
3.0%
ValueCountFrequency (%)
20 1
 
< 0.1%
19 1
 
< 0.1%
18 4
 
0.1%
17 1
 
< 0.1%
16 7
 
0.2%
15 9
 
0.2%
14 5
 
0.1%
13 16
0.4%
12 18
0.4%
11 38
0.9%

total_intl_charge
Real number (ℝ)

HIGH CORRELATION 

Distinct168
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.7696541
Minimum0
Maximum5.4
Zeros22
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size33.3 KiB
2024-04-30T07:20:04.016822image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.54
Q12.3
median2.78
Q33.24
95-th percentile3.94
Maximum5.4
Range5.4
Interquartile range (IQR)0.94

Descriptive statistics

Standard deviation0.74520414
Coefficient of variation (CV)0.26906036
Kurtosis0.70332127
Mean2.7696541
Median Absolute Deviation (MAD)0.48
Skewness-0.24167067
Sum11771.03
Variance0.5553292
MonotonicityNot monotonic
2024-04-30T07:20:04.261850image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3 75
 
1.8%
2.65 73
 
1.7%
3.08 73
 
1.7%
2.75 72
 
1.7%
2.94 71
 
1.7%
3.05 70
 
1.6%
2.73 69
 
1.6%
2.62 68
 
1.6%
2.84 66
 
1.6%
2.57 66
 
1.6%
Other values (158) 3547
83.5%
ValueCountFrequency (%)
0 22
0.5%
0.11 1
 
< 0.1%
0.3 2
 
< 0.1%
0.35 1
 
< 0.1%
0.54 2
 
< 0.1%
0.57 2
 
< 0.1%
0.59 2
 
< 0.1%
0.65 1
 
< 0.1%
0.68 1
 
< 0.1%
0.7 1
 
< 0.1%
ValueCountFrequency (%)
5.4 1
 
< 0.1%
5.32 2
< 0.1%
5.21 1
 
< 0.1%
5.18 1
 
< 0.1%
5.1 1
 
< 0.1%
5 1
 
< 0.1%
4.97 1
 
< 0.1%
4.94 1
 
< 0.1%
4.91 2
< 0.1%
4.86 3
0.1%

number_customer_service_calls
Real number (ℝ)

ZEROS 

Distinct10
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.5590588
Minimum0
Maximum9
Zeros886
Zeros (%)20.8%
Negative0
Negative (%)0.0%
Memory size33.3 KiB
2024-04-30T07:20:04.469991image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median1
Q32
95-th percentile4
Maximum9
Range9
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.3114335
Coefficient of variation (CV)0.84117001
Kurtosis1.6556188
Mean1.5590588
Median Absolute Deviation (MAD)1
Skewness1.0826916
Sum6626
Variance1.7198579
MonotonicityNot monotonic
2024-04-30T07:20:04.627987image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1 1524
35.9%
2 947
22.3%
0 886
20.8%
3 558
 
13.1%
4 209
 
4.9%
5 81
 
1.9%
6 28
 
0.7%
7 13
 
0.3%
9 2
 
< 0.1%
8 2
 
< 0.1%
ValueCountFrequency (%)
0 886
20.8%
1 1524
35.9%
2 947
22.3%
3 558
 
13.1%
4 209
 
4.9%
5 81
 
1.9%
6 28
 
0.7%
7 13
 
0.3%
8 2
 
< 0.1%
9 2
 
< 0.1%
ValueCountFrequency (%)
9 2
 
< 0.1%
8 2
 
< 0.1%
7 13
 
0.3%
6 28
 
0.7%
5 81
 
1.9%
4 209
 
4.9%
3 558
 
13.1%
2 947
22.3%
1 1524
35.9%
0 886
20.8%

churn
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size33.3 KiB
0
3652 
1
598 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4250
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 3652
85.9%
1 598
 
14.1%

Length

2024-04-30T07:20:04.825192image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-30T07:20:05.018026image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 3652
85.9%
1 598
 
14.1%

Most occurring characters

ValueCountFrequency (%)
0 3652
85.9%
1 598
 
14.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4250
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 3652
85.9%
1 598
 
14.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4250
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 3652
85.9%
1 598
 
14.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4250
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 3652
85.9%
1 598
 
14.1%

Interactions

2024-04-30T07:19:51.234557image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-30T07:19:01.296948image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-30T07:19:05.168910image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-30T07:19:08.251415image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-30T07:19:12.308682image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-30T07:19:16.142931image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-30T07:19:19.316583image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-30T07:19:22.823141image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-30T07:19:26.602528image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-30T07:19:29.735917image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-30T07:19:33.039323image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-30T07:19:37.599074image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-30T07:19:40.844863image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-30T07:19:44.109218image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-30T07:19:47.706548image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-30T07:19:51.432297image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-30T07:19:01.558013image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-30T07:19:05.362265image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-30T07:19:08.469615image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-30T07:19:12.505317image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-30T07:19:16.342058image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-30T07:19:19.531361image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-30T07:19:23.086204image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-30T07:19:26.794435image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-30T07:19:29.947764image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-30T07:19:33.237225image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-30T07:19:37.825006image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-30T07:19:41.061194image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-30T07:19:44.311961image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-30T07:19:47.986337image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-30T07:19:51.630949image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-30T07:19:01.816639image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-30T07:19:05.554201image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-30T07:19:08.673043image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-30T07:19:12.696609image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-30T07:19:16.538374image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-30T07:19:19.728181image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-30T07:19:23.408944image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-30T07:19:27.007699image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-30T07:19:30.174874image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-30T07:19:33.434048image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-30T07:19:38.027673image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-30T07:19:41.264307image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-30T07:19:44.521258image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-30T07:19:48.258628image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-30T07:19:51.839971image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-30T07:19:02.170152image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-30T07:19:05.782298image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-30T07:19:08.902771image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-30T07:19:12.906302image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-30T07:19:16.748354image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-30T07:19:19.941323image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-30T07:19:23.745074image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-30T07:19:27.223347image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-30T07:19:30.396086image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-30T07:19:33.665383image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-30T07:19:38.242134image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-30T07:19:41.486193image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-30T07:19:44.738747image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-30T07:19:48.556306image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-30T07:19:52.033984image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-30T07:19:02.442789image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-30T07:19:06.000437image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-30T07:19:09.104366image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-30T07:19:13.116406image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-30T07:19:16.950076image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-30T07:19:20.151389image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-30T07:19:24.075248image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-30T07:19:27.421283image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-30T07:19:30.621366image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-30T07:19:33.870733image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-30T07:19:38.456121image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-30T07:19:41.694297image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-30T07:19:44.943301image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-30T07:19:48.833227image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-30T07:19:52.241126image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-30T07:19:02.772309image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-30T07:19:06.196990image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-30T07:19:09.310335image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-30T07:19:13.331417image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-30T07:19:17.162987image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-30T07:19:20.368252image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-30T07:19:24.428013image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-30T07:19:27.636747image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-30T07:19:30.839450image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-30T07:19:34.071935image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-30T07:19:38.659983image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-30T07:19:41.916562image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-30T07:19:45.169225image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-30T07:19:49.114366image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-30T07:19:52.463090image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-30T07:19:03.058089image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-30T07:19:06.408289image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-30T07:19:09.550013image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-30T07:19:13.541257image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-30T07:19:17.376555image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-30T07:19:20.574917image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-30T07:19:24.686771image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-30T07:19:27.847733image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-30T07:19:31.061974image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-30T07:19:34.289591image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-30T07:19:38.892363image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-30T07:19:42.140154image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-30T07:19:45.378126image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-30T07:19:49.364386image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-30T07:19:52.664276image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-30T07:19:03.415231image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-30T07:19:06.620708image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-30T07:19:09.871953image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-30T07:19:13.745844image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-30T07:19:17.582933image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-30T07:19:20.791876image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-30T07:19:24.895811image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-30T07:19:28.053087image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-30T07:19:31.279904image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-30T07:19:34.496778image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-30T07:19:39.115217image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-30T07:19:42.366065image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-30T07:19:45.617928image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-30T07:19:49.566719image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-30T07:19:52.882757image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-30T07:19:03.671837image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-30T07:19:06.829026image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-30T07:19:10.205726image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-30T07:19:13.953816image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-30T07:19:17.785760image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-30T07:19:21.007822image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-30T07:19:25.105483image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-30T07:19:28.246981image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-30T07:19:31.499910image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-30T07:19:35.747458image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-30T07:19:39.325860image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-30T07:19:42.579451image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-30T07:19:45.836055image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-30T07:19:49.765842image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-30T07:19:53.109807image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-30T07:19:03.905470image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-30T07:19:07.039231image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-30T07:19:10.504822image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-30T07:19:14.881306image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-30T07:19:18.007879image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-30T07:19:21.235096image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-30T07:19:25.337524image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-30T07:19:28.463804image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-30T07:19:31.727322image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-30T07:19:36.100312image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-30T07:19:39.545722image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-30T07:19:42.805196image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-30T07:19:46.068151image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-30T07:19:49.976403image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-30T07:19:53.316694image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-30T07:19:04.112943image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-30T07:19:07.242865image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-30T07:19:10.840004image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-30T07:19:15.092036image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-30T07:19:18.230800image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-30T07:19:21.453199image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-30T07:19:25.547391image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-30T07:19:28.687427image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-30T07:19:31.946707image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-30T07:19:36.428175image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-30T07:19:39.763770image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-30T07:19:43.038965image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-30T07:19:46.303780image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-30T07:19:50.182512image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-30T07:19:53.529499image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-30T07:19:04.323739image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-30T07:19:07.459798image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-30T07:19:11.158432image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-30T07:19:15.304266image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-30T07:19:18.465042image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-30T07:19:21.672972image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-30T07:19:25.768393image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-30T07:19:28.889161image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-30T07:19:32.169226image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-30T07:19:36.730952image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-30T07:19:39.994668image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-30T07:19:43.255250image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-30T07:19:46.517382image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-30T07:19:50.406163image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-30T07:19:53.750157image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-30T07:19:04.542483image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-30T07:19:07.670357image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-30T07:19:11.502948image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-30T07:19:15.528523image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-30T07:19:18.691233image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-30T07:19:21.904771image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-30T07:19:25.979681image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-30T07:19:29.114175image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-30T07:19:32.396253image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-30T07:19:36.964356image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-30T07:19:40.231206image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-30T07:19:43.478253image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-30T07:19:46.809504image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-30T07:19:50.640282image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-30T07:19:53.971826image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-30T07:19:04.778187image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-30T07:19:07.894500image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-30T07:19:11.820689image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-30T07:19:15.754389image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-30T07:19:18.918832image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-30T07:19:22.189004image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-30T07:19:26.200140image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-30T07:19:29.328552image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-30T07:19:32.638678image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-30T07:19:37.202145image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-30T07:19:40.451448image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-30T07:19:43.707236image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-30T07:19:47.131284image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-30T07:19:50.854455image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-30T07:19:54.172233image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-30T07:19:04.969465image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-30T07:19:08.074179image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-30T07:19:12.110584image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-30T07:19:15.952863image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-30T07:19:19.115273image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-30T07:19:22.522055image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-30T07:19:26.394651image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-30T07:19:29.544134image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-30T07:19:32.839817image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-30T07:19:37.403011image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-30T07:19:40.653323image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-30T07:19:43.906878image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-30T07:19:47.446825image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-30T07:19:51.043411image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2024-04-30T07:20:05.190732image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
account_lengthchurninternational_plannumber_customer_service_callsnumber_vmail_messagestotal_day_callstotal_day_chargetotal_day_minutestotal_eve_callstotal_eve_chargetotal_eve_minutestotal_intl_callstotal_intl_chargetotal_intl_minutestotal_night_callstotal_night_chargetotal_night_minutesvoice_mail_plan
account_length1.0000.0000.000-0.007-0.0030.0190.0000.0000.005-0.014-0.0140.0190.0120.012-0.001-0.007-0.0070.000
churn0.0001.0000.2570.152-0.1080.0140.1770.177-0.0090.0750.075-0.0530.0480.048-0.0130.0470.0470.113
international_plan0.0000.2571.000-0.0160.0030.0120.0350.035-0.0040.0180.0180.0030.0210.0210.006-0.024-0.0240.000
number_customer_service_calls-0.0070.152-0.0161.000-0.024-0.021-0.007-0.0070.010-0.014-0.014-0.007-0.018-0.018-0.001-0.023-0.0230.038
number_vmail_messages-0.003-0.1080.003-0.0241.000-0.0110.0070.0070.0040.0150.015-0.0050.0020.0020.0060.0110.0110.998
total_day_calls0.0190.0140.012-0.021-0.0111.0000.0030.0030.0100.0090.0090.0080.0060.0060.0030.0020.0020.000
total_day_charge0.0000.1770.035-0.0070.0070.0031.0001.0000.005-0.015-0.015-0.000-0.024-0.024-0.0040.0020.0020.036
total_day_minutes0.0000.1770.035-0.0070.0070.0031.0001.0000.005-0.015-0.015-0.000-0.024-0.024-0.0040.0020.0020.036
total_eve_calls0.005-0.009-0.0040.0100.0040.0100.0050.0051.0000.0010.001-0.001-0.022-0.022-0.0160.0120.0120.000
total_eve_charge-0.0140.0750.018-0.0140.0150.009-0.015-0.0150.0011.0001.0000.0150.0060.0060.013-0.019-0.0190.017
total_eve_minutes-0.0140.0750.018-0.0140.0150.009-0.015-0.0150.0011.0001.0000.0150.0060.0060.013-0.019-0.0190.019
total_intl_calls0.019-0.0530.003-0.007-0.0050.008-0.000-0.000-0.0010.0150.0151.0000.0070.0070.004-0.020-0.0200.000
total_intl_charge0.0120.0480.021-0.0180.0020.006-0.024-0.024-0.0220.0060.0060.0071.0001.0000.006-0.002-0.0020.000
total_intl_minutes0.0120.0480.021-0.0180.0020.006-0.024-0.024-0.0220.0060.0060.0071.0001.0000.006-0.002-0.0020.000
total_night_calls-0.001-0.0130.006-0.0010.0060.003-0.004-0.004-0.0160.0130.0130.0040.0060.0061.0000.0110.0110.000
total_night_charge-0.0070.047-0.024-0.0230.0110.0020.0020.0020.012-0.019-0.019-0.020-0.002-0.0020.0111.0001.0000.031
total_night_minutes-0.0070.047-0.024-0.0230.0110.0020.0020.0020.012-0.019-0.019-0.020-0.002-0.0020.0111.0001.0000.031
voice_mail_plan0.0000.1130.0000.0380.9980.0000.0360.0360.0000.0170.0190.0000.0000.0000.0000.0310.0311.000

Missing values

2024-04-30T07:19:54.481999image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-30T07:19:54.982430image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

account_lengthinternational_planvoice_mail_plannumber_vmail_messagestotal_day_minutestotal_day_callstotal_day_chargetotal_eve_minutestotal_eve_callstotal_eve_chargetotal_night_minutestotal_night_callstotal_night_chargetotal_intl_minutestotal_intl_callstotal_intl_chargenumber_customer_service_callschurn
01070126161.612327.47195.510316.62254.410311.4513.733.7010
1137000243.411441.38121.211010.30162.61047.3212.253.2900
284100299.47150.9061.9885.26196.9898.866.671.7820
375100166.711328.34148.312212.61186.91218.4110.132.7330
41210124218.28837.09348.510829.62212.61189.577.572.0330
5147100157.07926.69103.1948.76211.8969.537.161.9200
6117000184.59731.37351.68029.89215.8909.718.742.3510
71411137258.68443.96222.011118.87326.49714.6911.253.0200
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